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Preclinical Alzheimer’s disease: a systematic review of

the cohorts underlying the concept

Stéphane Epelbaum, Rémy Genthon, Enrica Cavedo, Marie Habert, Foudil

Lamari, Geoffroy Gagliardi, Simone Lista, Marc Teichmann, Hovagim

Bakardjian, Harald Hampel, et al.

To cite this version:

Stéphane Epelbaum, Rémy Genthon, Enrica Cavedo, Marie Habert, Foudil Lamari, et al.. Preclinical Alzheimer’s disease: a systematic review of the cohorts underlying the concept. Alzheimer’s and Dementia, Elsevier, 2017, 13 (4), pp.454-467. �10.1016/j.jalz.2016.12.003�. �hal-01672859�

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Preclinical Alzheimer’s disease: a systematic review of the cohorts

underlying the concept

Stéphane Epelbauma,b, Rémy Genthona, Enrica Cavedoa, Marie Odile Habertb,c, Foudil

Lamarid, Geoffroy Gagliardia,b,, Simone Listaa,e,f, Marc Teichmanna,b, Hovagim Bakardjiana,e,f, Harald Hampela,b,f, Bruno Duboisa,b

a AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Département de Neurologie, Institut de la mémoire et de la maladie d’Alzheimer, Groupe Hospitalier Pitié-Salpêtrière, 47 Bd de l’Hôpital, 75013, Paris, France

b ICM - CNRS UMR 7225 - Inserm U 1127 - UPMC-P6 UMR S 1127, GH Pitié-Salpêtrière, 47 Bd de l'Hôpital, 75013, Paris, France

c AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Département de médecine nucléaire, Groupe Hospitalier Pitié-Salpêtrière, 47 Bd de l’Hôpital, 75013, Paris, France

d AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Laboratoire de Biochimie, Groupe Hospitalier Pitié-Salpêtrière, 47 Bd de l’Hôpital, 75013, Paris, France

e IHU-A-ICM - Paris Institute of Translational Neurosciences, Hôpital de la Pitié-Salpêtrière, 47 Bd de l'Hôpital, 75013, Paris, France

f AXA Research Fund & UPMC Chair, Paris, France

Abstract

Preclinical Alzheimer’s disease (AD) is a relatively recent concept describing an entity characterized by the presence of a pathophysiological biomarker signature characteristic for AD in the absence of specific clinical symptoms. There is rising interest in the scientific community to define such an early target population mainly due to failures of all recent clinical trials despite evidence of biological effects on brain amyloidosis for some compounds. A conceptual framework has recently been proposed for this preclinical phase of AD. However, few data exist on this silent stage of AD. We performed a systematic review in order to investigate how the concept is defined across studies. The review highlights the substantial heterogeneity concerning the three main determinants of preclinical AD: “normal cognition”, “cognitive decline” and “AD pathophysiological signature”. We emphasize the need for a harmonized nomenclature of the preclinical AD concept and standardized population-based and case-control studies using unified operationalized criteria.

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Introduction

The positivity of biomarkers of Alzheimer’s disease (AD) before the occurrence of first clinical symptoms and dementia, supports the concept that AD is a continuum, and that it could be diagnosed before its clinical expression [1]. Intervention at such an early stage of the disease is considered to improve the chance of success because it would target potentially still reversible and less established and extensive pathological processes. The lack of clinical efficacy of trials using monoclonal antibodies targeting amyloid at a mild or moderate stage of the illness is further encouragement to shift the attention to the preclinical stage of the disease.

The concept of a preclinical stage of AD emerged mainly from clinico-pathological studies describing apparently cognitively normal individuals with the possibility of AD hallmark lesions in the brain.[2-5] The International Working Group-2 (IWG-2) and later the National Institute on Aging-Alzheimer’s Association (NIA-AA) consortium each proposed a definition of the preclinical stage of AD [6, 7]. The recent release of consensual criteria should facilitate the harmonization and the quality of epidemiological and interventional research on preclinical AD [1].

Until now, little is known about the natural history of the preclinical state. Large epidemiological studies have been conducted or are still ongoing regarding the risk of dementia in the general population, but they are not strictly focusing on AD, and even less on the identification of subjects with the preclinical form of the disease using AD biomarkers (For review see [8] ).

Per definition, people with preclinical AD lack the classical symptoms of the disease. However, the NIA-AA defines a stage of preclinical AD, with “subtle cognitive decline” [7]. This is due to the fact that most longitudinal epidemiological studies show the occurrence of decline, mainly in terms of psycho-motor speed and executive functions, years before the diagnosis of dementia [9, 10]. There is no consensual definition for “subtle cognitive changes” (i.e. “normal cognition” and “cognitive decline”). Likewise, an AD physiopathological biomarker profile was not required for study inclusion in these studies. The present article, based on a systematic review of the literature on preclinical AD, aims at identifying the diagnostic approaches used by the leading groups in the field at this early stage of the disease. In particular three main issues concerning the concept of preclinical AD must be clarified: 1) the level of cognitive performance considered as “normal cognition” 2) the changes in cognitive performance considered as “cognitive decline”, and 3) the best biomarkers or the best combination of them able to identify the “AD pathophysiological signature” in vivo. This review could support future clinical research in the field especially if a disease modifying drug demonstrates its efficacy.

METHODS:

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The PubMed Database and ClinicalTrials.gov were searched for the terms “Preclinical Alzheimer’s disease”, “Preclinical Alzheimer disease”, “Presymptomatic Alzheimer’s disease”, “Presymptomatic Alzheimer disease”, “Asymptomatic Alzheimer’s disease”, “Asymptomatic Alzheimer disease”, up to June 2016, without any language restriction. The terms had to be in the title or even in the abstract of the manuscript in order to include articles that would only refer to the concept of preclinical AD without studying it.

Search Strategy Results and further classification of

studies

We identified 361 articles reporting “preclinical AD”. They were categorized as “reviews” (for review, conceptual and perspective articles), “out of topic” (when despite the title or abstract of the article, no preclinical AD subject was included in the study), “neuropathological” (when AD diagnosis was pathologically established in subjects who died within one year of a cognitive evaluation considered as unimpaired), “genetic” when the study dealt with cognitively healthy carriers of causative mutations for familial AD, and “biomarker” when they comprised a biomarker based definition of the AD pathophysiology. They were further stratified in “cross sectional” or “longitudinal”. Furthermore, we empirically chose to exclude articles with a sample size below 100 participants in order to focus on the major cohorts allowing for the study of the preclinical AD concept. The search strategy and distribution of the studies are reported in Fig 1. Fifty five studies from the “neuropathological”, “genetic” and “biomarker” groups satisfied the above criteria and have been investigated. From each study, the total number of participants according to their diagnosis (healthy control, preclinical AD, NIA-AA preclinical AD stages and when appropriate mild cognitive impairment (MCI) and AD dementia participants) were extracted as well as their mean age, the percentage of APOE ε4 carriers and the cohort study from which they derived. As “Suspected non AD Pathophysiology” (SNAP) for biomarker based studies and “Primary age related taupathy” (PART) for neuropathological studies are two concepts that arose from the more systematic use of AD biomarkers and the rising interest in the earlier stages of AD [11], their number in studies on Preclinical AD were also considered. Finally, the way to define “normal cognition”, “cognitive decline” and “AD pathophysiological signature” was analysed in each study. An overview of the studies’ population and methodologies are provided in Tables 1 & 2 respectively. The detailed description of the 55 studies and their methodology are provided in Supplementary Tables 1 and 2.

Cohorts allowing the study of preclinical AD

.

Thirteen different cohorts of cognitively normal individuals for the investigation of preclinical AD were identified from these 55 publications. Nine of them are monocentric and currently recruiting as they were developed in the context of a clinical-research setting with an observational period ranging from 3 to 20 years. Each cohort characteristics were extracted from the published studies and from the cohorts’ websites when available. Specifically, the latest published number of healthy elderly volunteers included in the cohort, the type of follow-up,(clinical routine or research), the mono or multicentric recruitment, the ethnicity and inclusion of minorities, the geographical origin of participants, the male/female ratios, the age of included participants, the selected criteria for normal cognition, the neuropsychological

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battery, the existence of cerebrospinal fluid biomarker or blood sampling, MRI, 18FDG-PET, Amyloid-PET and other biomarkers was reported (see Table 3 & Supplementary Table 3). The number of studies in this review categorized by the cohort they emerge from are detailed in Supplementary Fig 1. The diverse cognitive tests are also presented in Fig 2 to clearly depict their frequency of use in the 13 cohorts.

Clinical trials on preclinical AD

In addition to the observational cohorts described above, the ClinicalTrials.gov website was employed for a detailed research on the drug trials available on the preclinical AD population. All trials mentioning “preclinical AD” as a target population with pathophysiological markers of AD as inclusion criteria in their study design were considered. Three trials were identified, 2 of which concerning familial AD as described in Table 4. This relatively low number of trials is due to the fact that most (8/11) trials listed on the “Clinicaltrial.gov” webpage (but excluded from this review) pertaining to the “preclinical Alzheimer’s disease” search terms do not use pathophysiological markers at enrolment, thus being trials on the risk of

developing MCI or dementia rather than on the more precise “preclinical AD” concept.

RESULTS:

“Normal cognition”

The concept of “normal cognition” is controversial. It is indeed hard to define whether a given individual can be considered as cognitively normal. Usually this is achieved by comparing his psychometric performance to that of a predefined age and educational level matched group on specific tests. In this case, there is no reference to his own cognitive abilities prior to the assessment. This individual factor, requiring longitudinal follow-up prior to inclusion, is almost never accounted for in studies on preclinical AD. Moreover, in the 55 studies selected, five (9.1%) did not clearly specify what was considered as “normal cognition”. Twenty-one studies (38.2%) made use of the Clinical Dementia Rating scale (CDR) out of which thirteen (23.6%) used exclusively the CDR score equal to 0 to classify participants as cognitively healthy. The remaining 29 (52.7%) studies relied either on single Mini-mental State Examination (MMSE) or multiple cognitive tests, or on the clinical judgment of one investigators (see Table 2 for details). When cognitive tests were used, the clear definition of what was considered to be “pathological” was not always explicit. By contrast, in some cases it was described thoroughly [12]. MMSE scores when used as cut-off points between normality and impairment varied from 26 [13, 14] to 28 [15]. Interestingly, the MMSE cut-off scores, used in the studies, were higher than those necessary to be included in some of the cohorts (see Table 3 and below). Finally, the 3 clinical trials conducted on preclinical AD used different inclusion criteria (see Table 4).

Concerning the cohorts: The criterion used to define ‘normal cognition’ was heterogeneous as well. In seven out of the thirteen cohorts, the definition was based on the performance

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obtained on standard neuropsychological batteries. In the remaining cohorts, subjects were considered cognitively intact when they had a MMSE scores above 24 with a CDR score equal to 0 in the absence of depressive symptoms. The clinical and neuropsychological assessments were part of the study protocol for all considered cohorts, although the neuropsychological assessment used was not harmonized among cohorts (Fig. 3). The use of core biomarkers of AD was also heterogeneous. In most of the cohorts, the collection of biological and imaging markers was mainly restricted to a subsample of subjects. In addition to the physiopathological biomarkers, three studies collected EEG and three other reported post mortem neuropathological findings.

In terms of open source availability of data collected, not all of these studies are accessible to the scientific community. To our knowledge, the Alzheimer’s Disease neuroimaging Initiative (ADNI), the Australian Imaging, the Biomarkers and Lifestyle Flagship Study of Ageing (AIBL), the Harvard Aging Brain Study (HABS), the Charles F. and Joanne Knight Alzheimer's Disease Research Centre (Knight ADRC) at Washington University School of Medicine, the National Alzheimer's Coordinating Centre (NACC) database, and the Wisconsin Registry for Alzheimer's Prevention (WRAP) are the only databases on preclinical AD patients allowing external investigators to access data throughout online available platforms and after appropriate review of projects submitted.

“Cognitive decline/outcome”

The definition of cognitive decline, as previously emphasized by the NIA-AA guidelines and in clinical trials in preclinical AD descriptions [7, 16-18], also raises some issues: if it is too strict (e.g. going from a CDR equal to 0 to a CDR equal to 1), the number of individuals with “preclinical AD” progressing to “clinical AD” will be very low and will require long-term studies (years if not decades) to draw conclusions on risk factors and progression of preclinical AD. Conversely, if the definition encompasses any slight change in cognition over time (e.g. an increase of a few seconds in a timed psycho-motor speed test), the risk of a low specificity and high number of false positive rises (i.e. temporary cognitive impairment unrelated to AD and disappearing during longer follow-up). In the reviewed studies, the strategy to define cognitive decline was heterogeneous (see table 2 for details). In the three clinical trials, different tests were used to evaluate cognitive decline (see Table 4). Contrarily to the other “determinants” of preclinical AD, the cognitive decline is not mandatory for diagnosis. Both hypothetical frameworks of preclinical AD recognize that the diagnosis can be made when there is 1) a normal cognition and 2) markers of AD pathophysiology [1, 7]. However, evidencing a cognitive decline (even when cognition remains normal with respect to normative data) in an individual is a strong supportive argument of preclinical AD and is the basis on which the clinical trials in preclinical AD are being conducted [18].

“AD pathophysiological signature”

Three approaches can be of use to search for signs of AD pathophysiology in individuals with a normal cognition. The gold-standard one is the post-mortem brain examination which can be used to directly assess regional Aß and tau pathology loads and provide a neuropathological diagnosis [19, 20]. A limit of this method is that it only allows the study of subjects who died without any clinical impairment but it precludes the study of cognitive

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decline. Thus, rather than naming the concept “preclinical AD” in this type of study one could advocate the term “non-clinical AD pathology” or “silent AD pathology” as it is impossible to know if these subjects would have developed clinical symptoms if they have lived for a longer time. This neuropathological validation was performed in 4/55 (7.3%) of this review’s studies. The second method is the identification of a specific Mendelian autosomal dominant genetic mutation for familial AD (FAD). This allows studying preclinical early onset forms of AD as these mutations have a 100% penetrance so that all carriers will develop the disease. Moreover, the age of onset of symptoms in a mutation carrier is approximately the same as that of his parent. Cross sectional studies have been performed in these asymptomatic carriers to analyse the biomarker differences over time and to hypothesize their evolution [21]. A limitation is that the FAD population represents a minor fraction of all AD patients with differences in the expression, progression and pathophysiology of the disease such as the early age of onset. One out of the 55 studies (2%) used this method in our review. The third way to identify the underlying AD physiopathology relies on the use of biomarkers. According to the IWG criteria, only some markers of AD such as CSF biomarkers (Aß, tau or phosphorylated tau) and amyloid and tau positon emission tomography (PET) but not MRI nor functional imaging are considered as pathophysiological markers [6]. In the NIA-AA criteria, brain (especially hippocampal) atrophy on MRI or hypometabolism on 18FDG-PET are also considered as suitable biomarkers to identify AD as they reflect a neurodegeneration pattern compatible with the disease [7]. In the present review the more restrictive IWG criteria were used so that each of the selected studies can be considered as relying on specific markers to assess an “AD pathophysiological signature” (CSF and/or amyloid and tau PET assessments). However, to date, there is no consensual biomarker-based method universally recognized to define “AD pathophysiological signature”, such as prostate-specific antigen (PSA) values in prostate cancer or glucose values in diabetes [22]. In the studies reviewed herein, fourteen different definitions were applied for CSF biomarkers (CSF collection biomarkers assays, considered markers or panel of markers and cut-offs) and sixteen different definitions for amyloid PET (in terms of tracer, analytical methodology or threshold) out of the fifty biomarker based studies.

Discussion and strategy for the standardization and

harmonization of the Preclinical AD diagnostic and

follow-up procedure.

Following the problematic experience with the vastly heterogeneous application of prodromal “MCI” concepts to research studies and drug development programs in AD [23] and failures of recent large-scale trials aiming at slowing down progression of the disease in patients with mild to moderate AD [24, 25], great interest has developed towards the earlier phase of the disease. At present, numerous clinical trials include prodromal AD participants with different definitions [23]. In view of the evolving paradigm change, preclinical AD, a concept that could provide a valuable early time window for therapeutic intervention, is under much scrutiny. The standardization of the neuropsychological and biomarker evaluation required for its diagnosis is an important challenge for future studies [26]. This is supported by converging evidence toward the possible efficacy of disease modifying drugs in the early clinical stage of AD [24, 27]. We propose that three issues should be addressed consistently in upcoming research on preclinical AD: the definition and diagnostic procedures of “normal cognition”, “cognitive decline” and “AD physiopathological signature”. In our review, these three

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determinants are largely heterogeneous which contributes among other, less modifiable factors such as geography of recruitment, to a substantial variability from one study population to the other. For instance, the ratio of stage 1 and 2 preclinical AD is of 78 and 22% respectively in one study [28] and of 21 and 79% in another one [29] limiting the generalizability of each study’s findings. However, some homogeneity can also be evidenced. The CDR is the most commonly used tool used to define “normal cognition”, and frequently used to assess “cognitive decline”. Other endpoints are proposed that rely on various cognitive tests, diagnostic criteria for MCI or prodromal AD and since 2014, various composite cognitive scores. Compared to the CDR, these tests and composite scores offer the advantage of a finer delineation of the subtle cognitive changes that might occur many years before dementia is evidenced. On the other side, they are much more heterogeneous than the universally used CDR. Another issue with composite scores is their multiplicity. In the last two years, at least five different scores have been proposed relying on different methodologies (such as item response theory, or mean-to-standard deviation ratio) and including different tests [30-34]. Also, the use of subjective cognitive decline (SCD) was never considered as a marker of decline, even in studies focusing on memory complaints [15, 35-38] although it has been suggested that it might be a marker appearing late in the preclinical stage of the disease [9]. In fact, the main difference between “normal cognition” and “cognitive decline” could be drawn from differences in terms of relative risks (RR) to develop decline to milestones over time. For instance, an individual with SCD [39] should be considered as cognitively healthy since the RR of decline is low [40] and since the SCD condition is not specific of AD. The same can be said about psychomotor slowing and very mild executive changes which correspond to the “subtle cognitive changes” proposed by the NIA-AA. These symptoms arise many years before the dementia syndrome [9, 10, 38], are also non-specific and can be identified in other conditions such as mild vascular brain lesions [41] frontotemporal dementia [42] or even depression. On the other hand, when an individual has a low free recall in the free and cued selective reminding test (FCSRT) his risk to decline over the next years is high (>10 at 5 years) [43]. The specificity of the amnestic syndrome of the hippocampal type which is identifiable by this test allows for the classification of the subject in the clinical phase of AD (prodromal if it does not impact autonomy or dementia otherwise). This high risk profile and specificity for AD, even at its prodromal stage were the reasons why this test was recommended in the first IWG research criteria for AD diagnosis [44] Likewise, the “subtle cognitive changes”, namely attentional/psychomotor speed impairment, mild executive dysfunction should be operationalized as preclinical AD is more and more frequently studied. The chosen tests should be both the most frequently used ones by expert in the field and those which have demonstrated the best sensitivity to change over time in epidemiological studies on cognition in the elderly. The ten most frequently used tests in the 13 analysed cohorts are the Trail Making Test (TMT), Mini Mental State Evaluation (MMSE), Boston Naming Test, Verbal Fluency (animals), Clinical Dementia Rating scale (CDR), Logical Memory Test from the WMS-R, Rey Auditory Verbal Learning Test, Digit Span Forward and Backward from WMS-R, Digit Symbol Substitution Test (DSST) from WAIS-R and Verbal Fluency (letters). Performances below 1 standard deviations (SD) in cognitive tests and the individuals displaying these changes would still not be considered “cognitively impaired” in the absence of more specific symptoms. Studies conducted on the preclinical AD concept could be harmonized by 1) using tests to assess attention and psychomotor speed (such as the Digit Span Forward and DSST), executive functions (e.g. verbal fluencies, Trail making test), questionnaires to assess SCD, episodic memory (FCSRT), and global cognitive functioning (MMSE, CDR) (see table 5) and 2) by repeating these tests over time to identify “cognitive decline”. On a schematic point of view, this aspecific/low risk first symptoms vs. high risk/specific impairments can be represented as in Fig 4 and might determine possible

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interventions. This appears to be the only way to establish prospectively which test performance(s) is (are) the best specific predictor(s) for the transition from the preclinical to the prodromal stage of the disease. Regarding the “AD pathophysiological signature”, methods vary even more. This is most probably due to the recent development of these markers [45]. Of note for further studies, the recommendation to consider preclinical AD only in case of Aß AND tau positive markers points to the need to use either Aß and tau combinations of CSF biomarkers (12/50 studies with biomarkers in this review) or both Aß and tau PET tracers (0 article in this review) [20]. Variability in the choice of cognitive tests and pathophysiological markers as determinants of preclinical AD was maximized when the authors of the studies made use of open-source databases and was reduced in studies focusing on cohorts that were analysed by individual research groups under the supervision of the same principal investigator [46, 47]. The ongoing innovation (e.g. the replacement of 11C-PiB by 18Fluor labelled amyloid ligands [48, 49]) renders the process of standardization of biomarker

results challenging. There are, however, international efforts to homogenize cognitive [50] and biomarker practice in research studies [51-53]. The specific value of different markers has also been studied [54] but no study combining all these markers with further post-mortem brain examination to determine the individual and combined added values of these marker has, to our knowledge, ever been conducted. The value of downstream topographical biomarkers of progression (brain atrophy on MRI and hypometabolism on 18FDG-PET) [48] as possible

outcomes for decline should also be considered, notably in clinical trials [55]. Of course, it would be simplistic to consider preclinical AD as a homogeneous entity and the idea of proposing a “one size fits all” set of criteria may be problematic. But it is a necessary step to share results from different study groups. A unified definition of preclinical AD would of course not be definite but would evolve as different syndromic entities (eg fast vs. slow decliners) would emerge from ongoing studies. The fast paced innovation of biomarkers in the field also has to be considered. As new markers (such as blood based biomarkers) [56] are discovered and validated, their integration to the diagnostic algorithm of preclinical AD will have to be considered. In the end, a systems biology approach would be needed to propose a comprehensive set of definitions on as many preclinical AD variants as will be identified [57]. Of course, all these diagnostic processes rely on costly and invasive protocols that can, to date, only be proposed in the context of research projects in high income countries. This is reflected by the geographic location of the identified cohorts in this review and the low percentage of ethnic minorities among their participants. When a disease modifying treatment becomes available, the need to devise a pipeline of exams that is both safe and cost effective will be high. Specific neuro-economic studies should be conducted on the balance between the cost and adverse events due to a large scale screening for preclinical AD versus the long term benefit of early intervention at this stage of the disease.

One of the major limitation of this review is that it limits itself to the analysis of studies with more than one hundred participants. This choice was made empirically and the authors of this review recognize that many important insights for the field may be derived from studies with sizes below 100 subjects at this early exploratory and developmental period for the concept of preclinical AD. This choice was mainly made for one reason: to derive robust criteria for a disease or its risk factors you need an epidemiological study with a large number of participants and a long duration of follow-up, much like what the Framingham cohort brought to the cardiovascular field [58, 59]. Our cut-off of 100 participants ensured that we identified some of the preclinical Alzheimer’s field experienced centres with a total of 3854 individuals (sum of the total number of included participants in the latest published study of the 11 biomarker cohorts) with a mean (SEM) percentage of preclinical AD among them of 21.5 (2.2). Relatively to cohorts such as Framingham’s, the effort to harmonize the definition of preclinical AD in order be able to share information among centres appears even more needed.

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In conclusion, even if total standardization of different markers of cognition and AD signature cannot be achieved, the community should agree on the use of some general tools in order to provide robust knowledge on the preclinical AD concept. For instance DSST, CDR, FCSRT for the neurocognitive evaluation, CSF biomarker evaluation adapted to reference analytical procedures such as the Gothenburg measurements [52] and amyloid PET SUVR

standardization for instance to a centiloid scale [51]. Also, an operationalized description in these studies of the various subtle cognitive changes occurring in preclinical AD (as proposed in table 5) could lead to a better understanding of the path to decline to be used as markers in clinical trials. As the first important step has been taken when the scientific community agreed on the general principles to define preclinical AD [1], the AD community must take the next step toward a unified procedure to diagnose this disease stage.

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Figures & Tables

Figure 1: PRISMA (2009) flow diagram of article selection.

Figure 2: Number of studies categorized by the cohort from which they are derived Figure 3: Presentation of the different cognitive tests used in the thirteen cohorts

Figure 4: Schematic representation of Alzheimer’s disease (AD) clinical spectrum compared to that of Fronto-temporal dementia and Lewy body dementia. The three horizontal lines indicate a change in state from totally asymptomatic preclinical state (lowest quadrant) to preclinical state with subtle cognitive changes to prodromal to dementia (upper quadrant). The initial “preclinical” phase of the disease is represented as a unique triangle encompassing all of the diseases to reflect the difficulty to clinically distinguish one entity from the next at this stage. The five smaller triangles each correspond to one affection. The “…” indicate that the model can be extended with other neurocognitive affections. In the prodromal phase they are well separated as clinical symptoms are often specific of one disease. At the dementia stage, the overlap between these triangles indicate the association of diverse symptoms obfuscating distinct diagnosis. AD physiopathological biomarker status (displayed by the continuity of the yellow dotted line and the yellow triangle) is considered positive from the totally

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Table 1: Description of studies populations Table 2: Studies methodology

Table 3:Cohorts collecting cognitive and AD pathophysiological markers data in asymptomatic individuals allowing the study of the preclinical AD concept Table 4: Clinical trials in preclinical AD patients

Table 5: Proposed guidelines and nomenclature to operationalize Preclinical AD stages. Supplemental Table 1: Detailed Description of studies populations

Supplemental Table 2: Detailed Studies methodology

Supplemental Table 3: Cognitive batteries performed in the different cohorts

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TABLE 1 STUDIES POPULATIONS

N (%) or Mean (SEM) Cross Sectional

studies N=22 Longitudinal Studies N=29 Neuropathological studies N=4 Years of Publication

2014 13 (59) 21 (72) 0 (0)

Study population total size 331.5 (48.2) 261.1 (42) 866 (550)**

Age 71.4 (1,6) 68.1(1,3) 78.6 (3.3)

HC 158.4 (22.2) 164.3 (20.2) 184 (110.3)

HC percentage of total population 55.8 (3.6) 65.2 (3.2) 58 (8.2)

PC AD 83.6 (12.3) 65.4 (11.2) 111.3 (68.6)

PC AD percentage of total population 27.3 (1.5) 26.4 (1.4) 33.7 (5) NIA-AA PC AD Criteria [1] or [2]

N of studies using the conceptual framework (%)

8 (36.4) 7 (24.1) 1 (25)

Stage I* 54 (5.2) 53 (5.6) 24

Stage II* 41.4 (6.3) 43.6 (6.3) 28

SNAP percentage of total population 20.4 (1.7) 21.5 (1.6) 10

AP0E4 percentage of Total population 31 (2) 34.8 (1.5) 29 (1)

APOE4 percentage of PC AD 50.7 (3.7) 44.1 (3.3) 32.1 (0.9)

*% of PC AD. Stage III was only rarely applied (i.e. in 5 of 8 cross sectional studies and 4 of 7 longitudinal studies using this terminology) and so was not included in the table. ** One large study did not give any detail on the number of preclinical AD which accounts for the discrepancy between the Study population total size and the rest of the table figures for the Neuropathology column. PC AD = Preclinical Alzheimer’s disease, HC: Healthy control. [1] Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7:280-92.

[2] Jicha GA, Abner EL, Schmitt FA, Kryscio RJ, Riley KP, Cooper GE, et al. Preclinical AD Workgroup staging: pathological correlates and potential challenges. Neurobiol Aging.

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TABLE 2. STUDIES METHODOLOGY N (%) Cross Sectional (CS) Studies N = 22 Longitudinal (L) Studies N = 29 Neuropathology (N) Studies N=4 Criteria for Normal Cognition Available in N= 20 (90.9% of CS studies) Available in N= 27 (93.1% of L studies) Available in N= 3 (75% of N studies) CDR 4 (20) 8 (29.6) 1 (33.3) CDR+ 4 (30) 4 (14.8) 0 Cognitive battery 8 (40) 4 (14.8) 1 (33.3) MMSE 2 (10) 10 (37.1) 1 (33.3) Clinical Consensus 0 1 (3.7) 0 Criteria for Cognitive Decline Not applicable Available in N= 27 (93.1% of L studies) Available in N= 1 (25% of N studies) CDR - 6 (22.3) 1 (100) CDR+ - 1 (3.7) 0 Cognitive battery - 3 (11.1) 0 Composite scores - 12 (44.4) 0 Clinical consensus - 5 (18.5) 0 Criteria for AD pathophysiological signature CSF biomarkers 9 (40.9) 7 (24.2) 0 Amyloid PET 11 (50) 17 (58.6) 0 Both 2 (9.1) 4 (13.8) 0 Mutation 0 1 (3.4) 0 Neuropathology 0 0 4 (100)

CDR: Clinical dementia rating scale, CDR+: association of a Clinical dementia rating scale and at least one other neuropsychological test, Cognitive battery: association of at least two cognitive test, Clinical consensus: Adjudication by an expert committee of clinicians into one of three categories (normal cognition, mild cognitive impairment or dementia), CSF biomarkers: use of either

cerebrospinal fluid Aß1-42, tau, phosphorylated tau concentrations or a combination of these markers.

Amyloid PET : use of either PIB, florbetapir or flutemetamol. Mutation : evidence of the presenilin 1 (PSEN1) E280A mutation. Neuropathology: Neuropathological evidence of Alzheimer’s disease.

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Table 3. Cohor t N Ty p e D e si g n Cou ntry /sta te Eth nici ty /m ino riti es Po pul ati on (M /F; Age (Me an +/- SD or Ran ge) Cognit ive Integr ity Criteri a Nps y Batt ery CS F MRI se qu en ces 18 FD G-PE T Amy loid PET Blo od Other Biom arker s Cohor t Refer ence ADNI 1-GO-2 14 5 R Mu lt i USA Ca uca sia ns 93 % M/ F= 58 /4 2 55-90 MMSE > 24, CDR=0 , No-depres sed, MCI nor demen ted Yes Su bsa mp le Yes Su bsa mp le Subs amp le Yes NA Weine r et al. 2010 AMST ERDA M Deme ntia Cohor t 13 2 C Mo n o NL Not me nti on ne d M/ F = 56 /4 4 64 +/- 10 No CI based on a NRPSY Batter y Yes Su bsa mp le Su bsa mp le Su bsa mp le Subs amp le Su bsa mp le EEG Subsa mple van der Flier et al. 2014 AIBL study (Aust ralian Imagi ng, Biom arker s and Lifest yle study ) 42 3 R Mu lt i Aust ralia Not me nti on ne d M/ F= 42 /5 8 70 +/- 7 No CI based on a NRPSY Batter y Yes Su bsa mp le

Yes Yes Yes Yes EEG Subsa mple Ellis 2009 BIOC ARD (Pros pectiv e Study of Bioma rkers for Older Contr ols at Risk for Alzhei mer’s Diseas e) 30 2 R Mo n o USA, MD Not me nti on ne d M/ F : 41 /5 9 Mid dle-age Mattis Demen tia Rating Scale, Buschk e Selecti ve Remin ding Test (Busch ke, 1973), and Wechsl er Memor y Scale — Revise d (WMS– R; Wechsl er, 1987) perfor mance within the

Yes Yes Yes No Subs amp le Yes postm ortem neuro patho logic evalu ations in a subsa mle Green wood et al. 2005

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normal age-related range of scores BioFI NDER (Biom arkers For Identi fying Neuro degen erativ e Disor ders Early and Reliab ly cognit ively health y cohort ) 35 2 R Mu lt i Swede n mentNot ionne d M/F: 46/5 4 >60 MMSE 28-30 at screeni ng visit

Yes Yes Yes No Yes Yes Tau

PET http://biofind er.se/b iofinde r_coho rts/co gnitive ly-health y-elderly / BLSA (Balti more longit udina l study of Aging ) 10 4 R Mo n o USA, MD. 6% 73. Ca uca sia ns M/ F: 50. 5/ 49. 5 Mea n 77.3 year s No MCI or demen tia by clinical evalua tion (i.e. No substa ntial CI based on mental status screeni ng tests)

Yes No Yes No Yes Yes No https:/ /www. blsa.ni h.gov/ HABS (Harv ard Aging Brain Study ) 27 5 R M o n o USA, MA 81 % Ca uca sia ns M/ F : 41 /5 9 62– 90 GDS<1 1, CDR=0 , MMSE >25 and Norma l Perfor mance s at Logical Memor y delaye d recall Yes Su bsa mp le

Yes Yes Yes Yes NA Dagley A 2015 MCSA (May o Clinic Study of Aging 13 31 R M o n o USA, MN 98, 6% Ca uca sia ns M/ F : 46 /5 4 70– 90 year s CDR =0 ; 
Norm al functio nal status ;

Yes No No No No Yes No Robert s et al., 2008

and 2012

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) NRPSY testing within normal limits NACC (Nati onal Alzhe imer' s Coord inatin g Cente r datab ase) - R M o n o USA 80 % Ca uca sia ns M/ F : 43 /5 7 <40- >90 year s No CI based on a NRPSY Batter y descri ption report ed [Weint raub 2009] Yes No Su bsa mp le No No Su bsa mp le postm ortem neuro patho logic evalu ations in a subsa mle https:/ /www. alz.wa shingt on.edu Nun Study 678 C Mo n o USA, MN Ethnici ty not me nti on ed. Spe cifi city of the coh ort po pul ati on: Nu ns M/ F : 0/ 10 0 Mea n 85 year s NRPSY battery (Delay ed Word Recall, Word Recog nition; Word List Memor y; Verbal Fluenc y; Constr uction al Praxis; Boston Namin g; MMSE)

Yes No No No No Yes postm ortem neuro patho logic evalu ations Snowd on et al. 1996 SIGN AL 266 R Mu lt i Spai n Not me nti on ne d - 50-75 MMSE score ≥24 and normal memor y perfor mance on FCSRT Signifi cant impair ment in other cogniti ve domai ns exclud ed throug h a formal cogniti ve evalua tion.

Yes Yes Yes No Opti

onal Yes None Alcolea et al 2015

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WU-ADRC (Was hingt on Unive rsity' s Alzhe imer' s Disea se Resea rch Cente r study ) 34 0 R Mo n o USA, MS. 92% Ca uca sia ns M/ F: 45 /5 5

≥65 CDR=0 Yes Yes No No No Yes No Vos SJ et al., 2013 WRA P (Wisc onsin Regis try for Alzhe imer' s Preve ntion ) 18 4 R Mo n o USA, WI. 98% Ca uca sia ns M/ F : 29 /7 1 40-65 year s NRPSY battery (Sager 2005)

Yes No No No No Yes No La Rue A et al., 2008 ; Sager MA 2005

R= Research; C=Clinical; Mono= Monocentric; Multi= Multicentric; CI= Cognitive Impairment; GDS=Geriatric Depression Scale; CDR=Clinical Dementia Rating Scale; FCSRT: Free and Cued Selective Reminding Test; NRPSY=Neuropsychological; MMSE Mini Mental State Examination. NA=Not Applicable

For some monocentric studies the name of center is reported as some cohorts may be pooled in the publication.

1 - Weiner MW(1), Aisen PS, Jack CR Jr, Jagust WJ, Trojanowski JQ, Shaw L, Saykin AJ, Morris JC, Cairns N, Beckett LA, Toga A, Green R, Walter S, Soares H, Snyder P,Siemers E, Potter W, Cole PE, Schmidt M; Alzheimer's Disease Neuroimaging Initiative.The Alzheimer's disease neuroimaging initiative: progress report and future plans.. Alzheimers Dement. 2010 May;6(3):202-11.e7. doi: 10.1016/j.jalz.2010.03.007.

2 - van der Flier WM, Pijnenburg YA, Prins N, Lemstra AW, Bouwman FH, Teunissen CE, van Berckel BN, Stam CJ, Barkhof F, Visser PJ, van Egmond E, Scheltens P. Optimizing patient care and research: the Amsterdam Dementia Cohort. J Alzheimers Dis.

2014;41(1):313-27.

3 - Ellis KA, Bush AI, Darby D, De Fazio D, Foster J, Hudson P, Lautenschlager NT, Lenzo N, Martins RN, Maruff P, Masters C, Milner A, Pike K, Rowe C, Savage G, Szoeke C, Taddei K, Villemagne V, Woodward M, Ames D; AIBL Research Group. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease. Int Psychogeriatr. 2009 Aug;21(4):672-87.

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4 - Greenwood PM, Lambert C, Sunderland T, Parasuraman R.4Effects of apolipoprotein E genotype on spatial attention, working memory, and their interaction in healthy, middle-aged adults: results From the National Institute of Mental Health's BIOCARD study.

Neuropsychology. 2005 Mar;19(2):199-211. .

5 - Palmqvist S, Zetterberg H, Blennow K(2), Vestberg S, Andreasson U, Brooks DJ, Owenius R, Hägerström D, Wollmer P, Minthon L, Hansson O.Accuracy of brain amyloid detection in clinical practice using cerebrospinal fluid β-amyloid 42: a cross-validation study against amyloid positron emission tomography. JAMA Neurol. 2014 Oct;71(10):1282-9. doi: 10.1001/jamaneurol.2014.1358.

6 - The Baltimore Longitudinal Study on Aging (BLSA): https://clinicaltrials.gov/ct2/show/NCT00233272

7 - Dagley A, LaPoint M, Huijbers W, Hedden T, McLaren DG, Chatwal JP, Papp KV, Amariglio RE, Blacker D, Rentz DM, Johnson KA, Sperling RA, Schultz AP. Harvard Aging Brain Study: Dataset and accessibility. Neuroimage. 2015 Apr 3. pii: S1053-8119(15)00265-7. 8 - Roberts RO, Geda YE, Knopman DS, Cha RH, Pankratz VS, Boeve BF, Ivnik RJ,

Tangalos EG, Petersen RC, Rocca WA. The Mayo Clinic Study of Aging: design and sampling, participation, baseline measures and sample characteristics. Neuroepidemiology. 2008;30(1):58-69.

Roberts RO, Cha RH, Knopman DS, Petersen RC, Rocca WA. Postmenopausal estrogen therapy and Alzheimer disease: overall negative findings. Alzheimer Dis Assoc Disord. 2006 Jul-Sep;20(3):141-6.

9 - National Alzheimer’s Coordinating Center (NACC)

https://www.alz.washington.edu/cgibin/broker93?_service=naccnew9&_program=naccwww. pubrep1.sas&TYPEF=DISPLAYIDS

10 - Snowdon DA, Kemper SJ, Mortimer JA, Greiner LH, Wekstein DR, Markesbery WR. Linguistic ability in early life and cognitive function and Alzheimer's disease in late life. Findings from the Nun Study.JAMA. 1996 Feb 21;275(7):528-32.

11- SIGNAL: http://signalstudy.es/en/objectives.html

Alcolea D, Martínez-Lage P, Sánchez-Juan P, Olazarán J, Antúnez C, Izagirre A, Ecay-Torres M, Estanga A, Clerigué M, Guisasola MC,

Sánchez Ruiz D, Marín Muñoz J, Calero M, Blesa R, Clarimón J, Carmona-Iragui M, Morenas-Rodríguez E, Rodríguez-Rodríguez E, Vázquez

Higuera JL, Fortea J, Lleó A. Amyloid precursor protein metabolism and inflammation markers in preclinical

Alzheimer disease. Neurology. 2015 Aug 18;85(7):626-33. doi: 10.1212/WNL.0000000000001859. Epub 2015

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12- Vos SJ, Xiong C, Visser PJ, Jasielec MS, Hassenstab J, Grant EA, Cairns NJ, Morris JC, Holtzman DM, Fagan AM. Preclinical Alzheimer's disease and its outcome: a longitudinal cohort study. Lancet Neurol. 2013 Oct;12(10):957-65.

13- Sager MA, Hermann B, La Rue A. Middle-aged children of persons with Alzheimer's disease: APOE genotypes and cognitive function in the Wisconsin Registry for Alzheimer's Prevention. J Geriatr Psychiatry Neurol. 2005 Dec;18(4):245-9.

La Rue A, Hermann B, Jones JE, Johnson S, Asthana S, Sager MA. Effect of parental family history of Alzheimer's disease on serial position

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Reference Cohort(s) used for the study [or center for

unnamed monocentric studies)] N Total A ge N HC N (% total populatio n) N PC N (% total popula tion) stage 1 N (% of PC) stage 2 N (% of PC) stag e 3 N (% of PC) N AD N (% of total popu latio n) N SNAP or PART N (% of total popula tion) N MCI N (% of total popul ation) N other N (% of total populati on) ApoE 4+ in total popul ation N (%) ApoE4+ in PC population N (%) CROSS-SECTIONAL STUDIES (Morris, Roe et al. 2010) WU-ADRC 241 66 ,8 - 44 (18) - - - 82 (34) - (Pike, Ellis et al. 2011) AIBL 177 70 119 (67) 58 (33) - - - 37 (64) (Mielke, Wiste et al. 2012) MCSA 483 - 332 (69) 151 (31) - - - 121 (25) - (Amariglio, Becker et al. 2012) HABS 131 73 ,5 97 (74) 34 (26) - - - - (Jack, Knopman et al. 2012) MCSA 450 78 193 (43) 139 (31) 70 (50) 56 (40) 13 (9) 42 (9) 103 (23) - 15 (3) 117 (26) - (Harrington, Chiang et al. 2013) [Pasadena (Cal)] 149 78 36 (24) 34 (23) - - - 29 (19) - 40 (27 ) 10 (7) - - (Whitwell, Tosakulwong et al. 2013) Mayo Clinic ADRC cohort or MCSA 318 80 115 (36) 115 (36) - - - 88 (28 ) - - 44 (38) (Ju, McLeland et al. 2013) WU-ADRC 142 65 ,6 110 (77) 32 (23) - - - 52 (36.6) 18 (56.2) (Knopman, Jack et al. 2013) MCSA 430 78 191 (44) 137 (32) 68 (50) 56 (41) 13 (9) - 102 (24) - - 107 (25) 62 (45) (Brier, Thomas et al. 2014) WU-ADRC 297 68 200 (67) 97 (33) - - - - (Brier, Thomas et al. 2014) WU-ADRC 326 69 132 (40) 59 (18) 46 (78) 13 (22) - 31 (9) - 90 (28 ) 14 (4) - - (Jack, Wiste et al. 2014) MCSA 985 74 503 (51) 352 (36) 213 (60) 130 (40) - - 139 (14) - - 256 (26) 134 (38) (Racine, Adluru et al. 2014) WRAP 139 60 ,6 112 (81) 27 (19) - - - 16 (59) (Fortea, Vilaplana et al. 2014) ADNI 145 73 ,4 74 (51) 39 (27) 8 (21) 31 (79) - - 32 (22) - - - - (Wang, Benzinger et al. 2015) WU-ADRC 188 73 - - - - (36) (Doherty, Schultz et al. 2015) WRAP 109 60 ,7 74 (68) 35 (32) - - - 45 (41) 19 (54) (Valech, Mollica et al. 2015) [Barcelona] 111 59 (53) 19 (24) - - - 10 (9) - 23 (21 ) - - -

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(Jack, Wiste et al. 2015) MCSA 1331 71 - - - 27 - (Alcolea, Martinez-Lage et al. 2015) SIGNAL 266 58 .8 203 (76.3) 36 (13.5) 26 (72) <10 (28) <10 (28) 0 27 (10.1) 0 0 67 (25) 20 (56) (Papp, Amariglio et al. 2015) HABS 260 73 126 (48) 70 (27) 32 (46) 38 (54) - - 64 (25) - - - - (Hassenstab, Chasse et al. 2016) WU-ADRC 264 72 177 (67) 87 (33) - - - 83 (31) 44 (51) (Voevodskaya , Sundgren et al. 2016) BioFINDER 352 72 156 (44) 108 (31) 59 (55) - 49 (45) - - 88 (25 ) - 142 (40) 62 (57) LONGITUDINAL STUDIES (Morris, Roe et al. 2009) WU-ADRC 159 71 ,5 - - - - (Craig-Schapiro, Perrin et al. 2010) WU-ADRC 340 71 - - - - (Desikan, McEvoy et al. 2012) ADNI 115 76 46 (40) 41 (36) 20 (49) 21 (51) - - 19 (17) - - 27 (23.3) 20 (48.8) (Knopman, Jack et al. 2012) MCSA 286 79 127 (44) 90 (31) 41 (46) 39 (43) 7 (8) - 69 (24) - - 74 (26) 36 (40) (van Harten, Smits et al. 2013) Amsterdam dementia cohort 132 61 80 (60) 21 (16) 11 (52) 10 (48) - - 31 (23) - - 54 (41) (Vos, Xiong et al. 2013) WU-ADRC 311 72 .9 129 (41) 96 (31) 47 (49) 36 (38) 13 (14) 0 (0) 72 (23) - 14 (5) 106 (34) 49 51 (Stark, Roe et al. 2013) WU-ADRC 119 74 .4 101 (85) 18 (15) - - - - (Lim, Villemagne et al. 2013) AIBL 165 71 ,4 116 (70) 49 (30) - - - 70 (42,4) 32 (65,3) (Lim, Maruff et al. 2014) AIBL 333 70 249 (75) 84 (25) - - - 109 (32,7) - (Mormino, Betensky et al. 2014) HABS 166 74 81 (49) 47 (28) 19 (40) 28 (60) - - 38 (23) - - 50 (30) 27 (58) (Donohue, Sperling et al. 2014) ADNI/AIBL 97/16 4 75 ,8 1/ 71 ,3 7 60/114(62/ 70) 37/50 (38/30) - - - 24/70 (25/4 3) - (Ayutyanont, Langbaum et al. 2014) E280A Antioquia cohort 134 44 78 (58) 56 (42) - - - - (Mormino, Betensky et al. 2014) HABS/ADNI/A IBL 490 75 355 (72) 135 (28) - - - 225 (46) 67 (50)

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(Pietrzak, Lim et al. 2015) AIBL 333 70 249 (75) 84 (25) - - - 109 (32,7) - (Pietrzak, Lim et al. 2015) AIBL 333 70 249 (75) 84 (25) - - - 109 (32,7) - (Nation, Edmonds et al. 2015) ADNI 877 72 ,6 - - - 167 (19) 602 (69) - 360 (41) - (Thai, Lim et al. 2015) AIBL 317 69 .9 - 76 (24) - - - - 104 (33) 45 (59) (Pettigrew, Soldan et al. 2015) BIOCARD 302 56 .6 240 (79) 62 (21) - - - - 94 (32) 21 (36) (Sutphen, Jasielec et al. 2015) WU-ADRC 169 60 .7 118 (70) 51 (30) - - - - 61(36 ) - (Papp, Mormino et al. 2016) HABS 275 205 (75) 70 (25) - - - - (Soldan, Pettigrew et al. 2016) BIOCARD 222 56 .9 102 (46) 74 (33) 46 (62) 28 (38) - - 46 (21) - - 73 (33) - (Racine, Koscik et al. 2016) WRAP and Wisconsin ADRC 175 59 76 (43) 54 (31) - - - - 45 (26) - - 79 (45) 22 (15) (Pascoal, Mathotaarach chi et al. 2016) ADNI 120 74 .9 - - - - (Vos, Gordon

et al. 2016)i WU-ADRC 212 66 .1 imaging: 127 (60), CSF:114 (54) imagin g: 45 (21), CSF:58 (27) imagi ng: 26, CSF:4 2 imag ing: 19, CSF: 16 - - 40 (19) - - 70 (33) 37 (17) (Bilgel, Prince et al. 2016) BLSA 104 77 - - - - (Brier, McCarthy et al. 2016) WU-ADRC 157 61 .6 131 (83) 26 (17) - - - - 49 (31.2) 17 (61.4) (Clark, Racine et al. 2016) WRAP 184 58 .6 156 (84.8) 28 (15.2) - - - - 73 (40) 12 (43) (Harrington, Gould et al. 2016) AIBL 359 69 .7 278 (77) 81 (22.6) - - - - (Lim, Snyder et al. 2016) AIBL 423 69 ,4 326 (77) 97 (23) - - - 115 (27) 51 (52) NEUROPATHOLOGICAL STUDIES (Jicha, Abner et al. 2012) UK-ADC (Kentucky) 126 85 ,1 59 (47) 54 (43) 13 (24) 15 (28) 26 (48) 14 (11) 13 (10) 24 (19) - 38 (30) 18 33 (Abner, Kryscio et al. 2011)

NACC and Nun Study

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TABLE 1. DESCRIPTION OF STUDIES POPULATIONS

ADNI

:

Alzheimer’s Disease Neuroimaging Initiative ; AIBL

:

Australian Imaging, Biomarkers and Lifestyle Flagship Study of

Ageing; BIOCARD: Biomarkers of Cognitive Decline Among Normal

Individuals; HABS: Harvard Aging Brain Study

;

MCSA

:

Mayo

Clinic Study of Aging Mayo Clinic ADRC: Mayo Clinic Alzheimer

Disease Research Center

;

NACC

:

National Alzheimer's Coordinating

Center database; SIGNAL study: Spanish project on biomarkers

in the

preclinical phase of Alzheimer Disease (AD). UK-ADC: University of

Kentucky, Alzheimer Disease Center ; BioFINDER: Biomarkers For

Identifying Neurodegenerative Disorders Early and

Reliably (Sweden); VA SanDiego : Veteran Administration San Diego,

CAL ; WU-ADRC

:

Charles and Joanne Knight Alzheimer's Disease

Research Center at Washington University in Saint Louis

;

WRAP

:

Wisconsin Registry for Alzheimer's Prevention. Multicentric autopsy

study: All autopsy brains collected from individuals who died in

university or municipal Hospitals in Germany (Bonn, Frankfurt/Main,

Mainz, Offenbach/Main, Ulm), USA (Little Rock, AR), the United

Kingdom (Newcastle upon Tyne), or Austria (Vienna)

HC : Healthy Controls

PC: Preclinical

AD : Alzheimer Disease

MCI : Mild Cognitive Impairment

SNAP : Suspected Non-Amyloid Pathology

PART : Primary Age Related Taupathy

Stage 1-3: according to NIA-AA proposed classification of preclinical

AD

Abner, E. L., R. J. Kryscio, F. A. Schmitt, K. S. Santacruz, G. A. Jicha, Y. Lin, J. M. Neltner, C. D. Smith, L. J. Van Eldik and P. T. Nelson (2011). ""End-stage" neurofibrillary tangle pathology in preclinical Alzheimer's disease: fact or fiction?" J Alzheimers Dis 25(3): 445-453. (Riley, Jicha et al. 2011) UK-ADC (Kentucky) 121 76 ,1 89 (74) 32 (26) - - - 34 (28,1) 10 (31,2) (Thal, von Arnim et al. 2013) Multicentric autopsy study. 766 74 ,7 404 (53) 248 (32) - - - 114 (15) - - - - -

Figure

Table 1: Description of studies populations  Table 2: Studies methodology
TABLE 1 STUDIES POPULATIONS
TABLE 2. STUDIES METHODOLOGY  N (%)  Cross  Sectional  (CS)  Studies  N = 22  Longitudinal (L) Studies N = 29  Neuropathology (N) Studies N=4  Criteria for   Normal   Cognition Available in N= 20   (90.9% of CS  studies)  Available in N= 27  (93.1% of L st
Table 3.   Cohor t  N  Ty p e  Desig n  Cou ntry/state  Ethnicity /mino riti es  Po pulati on (M /F;   Age (Mean +/- SD or Ran ge)  Cognitive Integrity  Criteria  Npsy  Battery  CSF  MRI sequen ces  18 FDG-PET  Amyloid  PET  Blood  Other Biomarkers  Cohort
+2

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